{"id":4326,"date":"2023-04-25T13:54:20","date_gmt":"2023-04-25T13:54:20","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=4326"},"modified":"2023-05-18T05:40:36","modified_gmt":"2023-05-18T05:40:36","slug":"misty-blowers","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/misty-blowers\/","title":{"rendered":"Misty Blowers"},"content":{"rendered":"\t\t
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Misty Blowers
\nChief Technology Officer, Datalytica

\nIndustrial AI<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\"\"<\/p>

June 5, 4:45pm
Location: Magnolia<\/h5>

Machine Learning Decision Support Systems for Event Detection<\/span><\/h4>

Detection of specific events in real-time longitudinal data is often critical for industrial optimization. This lecture will focus on the development of a machine learning capability to minimize the downtime of a paper machine in a recycling box plant that results from paper breaks. This novel approach generated significant value and was tailored to a specific environment that assists a company in making consumer grade product from post-consumer waste. This value was realized despite considerable variability and noise inherent to the operational environment. By fusing historical operator logs that attributed the causality of the breaks in a way that help maximize the probability of detection while minimizing false alarm rates, a machine learning enabled decision support tool provided 25-30% improvement on paper-break detections over traditional threshold based alerting methods. Similar approaches can be used to improve production across a wide-variety of industry sectors.<\/span><\/p>

\"\"<\/a>Dr. Misty Blowers currently serves as the Chief Technology Officer for Datalytica, a service disabled veteran owned small business that provides technical guidance to various government agencies in the DC area. Prior to this role, she served as the Chief Technology Officer for the United States Marine Corps.\u00a0 In this position she was responsible for Service level coordination for all artificial intelligence related activities, directed the transition of emerging technologies to the warfighters, and oversaw the development of the Marine Corps Information Environment Enterprise.\u00a0 Dr. Blowers started her career as a Paper Science Engineer after graduating from the Paper Science Engineering curriculum at the State University of New York at Syracuse University.\u00a0 Her early experience developing models and simulations for a pulp and paper based equipment supplier inspired her to further pursue a career as a computer scientist. After earning her Master\u2019s of Science degree in Computer Science at Syracuse University in 2003, she went to work for the US Air Force Research Laboratory.\u00a0 Since that time, she has led large defense programs on autonomy and artificial intelligence, developed two patented software based technologies, and served in technical advisement rolls to some of the highest level officials across the US Department of Defense. She has delivered technologies that increase security, strengthen military forces, serve public needs, and support critical military missions. Her return to the chemical process control industry has allowed her to bring knowledge gained from highly contested environments back to a manufacturing domain that is challenged with variability, complexity, and uncertainty.\u00a0\u00a0<\/span><\/p>

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Misty Blowers Chief Technology Officer, Datalytica Industrial AI June 5, 4:45pmLocation: Magnolia Machine Learning Decision Support Systems for Event Detection Detection of specific events in real-time longitudinal data is often critical for industrial optimization. This lecture will focus on the development of a machine learning capability to minimize the downtime of a paper machine in […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-4326","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/4326","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=4326"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/4326\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=4326"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}